arXiv subjects

Jawaher Kaldari

Publications and source records attributed to Jawaher Kaldari.

5 recordsLinked to original sources

arXiv2026

AML-QKD: Adaptive Machine Learning Framework for Real-time Parameter Tuning in QKD

Despite the robust security guarantees of Quantum Key Distribution (QKD), practical deployment is hindered by dynamic channel noise and complex parameter optimization. We propose AML-QKD, a protocol-agnostic machine learning framework designed to maximize the Secure Key Rate (SKR) and minimize the Quantum Bit Error Rate (QBER) across the BB84, E91, and COW protocols. AML-QKD integrates Temporal Convolutional Networks (TCNs) for short-horizon forecasting of channel fluctuations, using a Proximal Policy Optimization (PPO) agent for real-time parameter tuning, while strictly adhering to composable security constraints. Simulations under realistic depolarizing and amplitude-damping noise demonstrate a 14-25% increase in median SKR and a reduction in median QBER from 3.0% to 1.5%. Furthermore, an exploratory Quantum Reinforcement Learning (QRL) extension reveals a distinct quantum advantage for entanglement-based protocols (E91), achieving a 29.2% throughput gain by natively processing non-local correlations. Our findings suggest that AML-QKD can offer a potentially resilient, security-preserving control architecture for next-generation quantum networks.

quant-ph↗

arXiv2026

Challenge-Response Quantum Reinforcement Learning with Application to Quantum-Assisted Authentication

Quantum reinforcement learning (QRL) has emerged as a promising research direction that integrates quantum information processing into reinforcement learning frameworks. While many existing QRL studies apply quantum agents to classical environments, it has been realized that the potential advantages of QRL are most naturally explored in environments that exhibit intrinsically quantum characteristics, where the agent's observations and interactions arise from quantum processes. In this work, we propose a quantum reinforcement learning environment formulated as a challenge-response task with hidden information. In the proposed environment, Alice encodes a classical bit into the parameters of a quantum circuit, while Bob, with a trained reinforcement learning agent, interacts with a limited number of quantum state copies to infer the hidden bit. The agent must select measurement strategies and decide when to terminate the interaction under explicit resource constraints. To study the solvability of the proposed environment, we consider three agents: a purely classical agent, a lightweight hybrid agent and a deep hybrid agent. Through experiments, we analyze the trade-off between inference accuracy and quantum resource consumption under varying interaction penalties. Our results show that the lightweight hybrid agent achieves reliable inference using as few as two quantum state copies, outperforming both the classical baseline and the deep hybrid agent across both high and low-penalty regimes. We further evaluate robustness under realistic quantum noise models and discuss the relevance of the proposed environment for security-oriented applications, including quantum-assisted authentication.

quant-ph↗

arXiv2025

Quantum Reinforcement Learning: Recent Advances and Future Directions

As quantum machine learning continues to evolve, reinforcement learning stands out as a particularly promising yet underexplored frontier. In this survey, we investigate the recent advances in QRL to assess its potential in various applications. While QRL has generally received less attention than other quantum machine learning approaches, recent research reveals its distinct advantages and transversal applicability in both quantum and classical domains. We present a comprehensive analysis of the QRL framework, including its algorithms, architectures, and supporting SDK, as well as its applications in diverse fields. Additionally, we discuss the challenges and opportunities that QRL can unfold, highlighting promising use cases that may drive innovation in quantum-inspired reinforcement learning and catalyze its adoption in various interdisciplinary contexts.

quant-ph↗

arXiv2025

Feature Prediction in Quantum Graph Recurrent Neural Networks with Applications in Information Hiding

Graphs are a fundamental representation of complex, nonlinear structured data across various domains, including social networks and quantum systems. Quantum Graph Recurrent Neural Networks (QGRNNs) have been proposed to model quantum dynamics in graph-based quantum systems, but their applicability to classical data remains an open problem. In this paper, we leverage QGRNNs to process classical graph-structured data. In particular, we demonstrate how QGRNN can reconstruct node features in classical datasets. Our results show that QGRNN achieves high feature reconstruction accuracy, leading to near-perfect classification. Furthermore, we propose an information hiding technique based on our QGRNN, where messages are embedded into a graph, then retrieved under certain conditions. We assess retrieval accuracy for different dictionary sizes and message lengths, showing that QGRNN maintains high retrieval accuracy, with minor degradation as complexity increases. These findings demonstrate the scalability and robustness of QGRNNs for both classical data processing and secure information hiding, paving the way for quantum-enhanced feature extraction, privacy-preserving computations, and quantum steganography.

quant-ph↗

arXiv2023

Hybrid PLS-ML Authentication Scheme for V2I Communication Networks

Vehicular communication networks are rapidly emerging as vehicles become smarter. However, these networks are increasingly susceptible to various attacks. The situation is exacerbated by the rise in automated vehicles complicates, emphasizing the need for security and authentication measures to ensure safe and effective traffic management. In this paper, we propose a novel hybrid physical layer security (PLS)-machine learning (ML) authentication scheme by exploiting the position of the transmitter vehicle as a device fingerprint. We use a time-of-arrival (ToA) based localization mechanism where the ToA is estimated at roadside units (RSUs), and the coordinates of the transmitter vehicle are extracted at the base station (BS).Furthermore, to track the mobility of the moving legitimate vehicle, we use ML model trained on several system parameters. We try two ML models for this purpose, i.e., support vector regression and decision tree. To evaluate our scheme, we conduct binary hypothesis testing on the estimated positions with the help of the ground truths provided by the ML model, which classifies the transmitter node as legitimate or malicious. Moreover, we consider the probability of false alarm and the probability of missed detection as performance metrics resulting from the binary hypothesis testing, and mean absolute error (MAE), mean square error (MSE), and coefficient of determination $\text{R}^2$ to further evaluate the ML models. We also compare our scheme with a baseline scheme that exploits the angle of arrival at RSUs for authentication. We observe that our proposed position-based mechanism outperforms the baseline scheme significantly in terms of missed detections.

eess.SP↗